Evidence generation, decision making, and consequent growth in health disparities

Abstract

Evidence is valuable because it informs decisions to produce better outcomes. However, the same evidence that is complete for some individuals or groups may be incomplete for others, leading to inefficiencies in decision making and growth in disparities in outcomes. Specifically, the presence of treatment effect heterogeneity across some measure of baseline risk, and noisy information about such heterogeneity, can induce self-selection into randomized clinical trials (RCTs) by patients with distributions of baseline risk different from that of the target population. Consequently, average results from RCTs can disproportionately affect the treatment choices of patients with different baseline risks. Using economic models for these sequential processes of RCT enrollment, information generation, and the resulting treatment choice decisions, we show that the dynamic consequences of such information flow and behaviors may lead to growth in disparities in health outcomes across racial and ethnic categories. These disparities arise due to either the differential distribution of risk across those categories at the time RCT results are reported or the different rate of change of baseline risk over time across race and ethnicity, even though the distribution of risk within the RCT matched that of the target population when the RCT was conducted. We provide evidence on how these phenomena may have contributed to the growth in racial disparity in diabetes incidence.